Growth Experiments is a complete platform to track ideas, hypotheses and results.
As the shift to digital accelerates with each passing day, marketers are working hard to find the most effective strategies for search engine optimization. A big part of success here is understanding of how users respond to your website. And the best way to find this information involves a range of website testing methods. These tests enable us to experiment with multiple factors and extract the best outcome. By putting a control page against a test group, we can check for impact to conversions and UX. A/B and multivariate testing are popular testing styles, helping thousands of SEO experts globally. This post will discuss the right way of performing these tests and also includes some bonus tips. So, let’s begin:
Retrieving the page source of a website under scrutiny is a day-to-day task for most test automation engineers. Analysis of the page source helps eliminate bugs identified during regular website UI testing, functional testing, or security testing drills. In an extensively complex application testing process, automation test scripts can be written in a way that if errors are detected in the program, then it automatically. This is an easy way to trace, fix logical and syntactical errors in the front-end code. In this article, we first understand the terminologies involved and then explore how to get the page source in Selenium WebDriver using Python.
Product Managers develop hypotheses on an almost daily basis. It’s a critical part of the job. They develop hypotheses about new features, changes to UX, even the specific copy that best conveys to a customer what the customer should do next. Many times, however, especially at smaller companies or startups, these changes are simply “rolled out” or deployed to 100% of visitors/users/customers without a second thought.
<p>A/B testing is the process of comparing two versions of a web page, email, or other marketing assets, measuring the difference in performance. This is the page dedicated to sharing test results that might inspire other growth hackers to start running their own experiments and improve conversion rates.</p>